Cellular Potts Models: Multiscale Extensions and Biological Applications

Marco Scianna, Luigi Preziosi

March 26, 2013 by Chapman and Hall/CRC
Reference - 301 Pages - 19 Color & 118 B/W Illustrations
ISBN 9781466514782 - CAT# K15009
Series: Chapman & Hall/CRC Mathematical and Computational Biology


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  • Represents the first book entirely devoted to cellular Potts models and applications
  • Starts with basic models that are progressively extended to introduce the latest developments
  • Covers both biological and informatics aspects
  • Illustrates the models through real examples in biology and biomedicine
  • Describes popular model packages and how to build your own code in the appendices


A flexible, cell-level, and lattice-based technique, the cellular Potts model accurately describes the phenomenological mechanisms involved in many biological processes. Cellular Potts Models: Multiscale Extensions and Biological Applications gives an interdisciplinary, accessible treatment of these models, from the original methodologies to the latest developments.

The book first explains the biophysical bases, main merits, and limitations of the cellular Potts model. It then proposes several innovative extensions, focusing on ways to integrate and interface the basic cellular Potts model at the mesoscopic scale with approaches that accurately model microscopic dynamics. These extensions are designed to create a nested and hybrid environment, where the evolution of a biological system is realistically driven by the constant interplay and flux of information between the different levels of description. Through several biological examples, the authors demonstrate a qualitative and quantitative agreement with the relative experimental data.

The cellular Potts model is increasingly being used for the mathematical modeling of a wide range of biological phenomena, including wound healing, tumor growth, and cancer cell migration. This book shows how the cellular Potts model can be used as a framework for model building and how extended models can achieve even better biological practicality, accuracy, and predictive power.